US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Manage and mentor a Software Engineering team, owning the roadmap and architecture for applied AI, low-latency scoring, and automated evaluation.
Lead Knowledge Library and merchant control engineering, building advanced RAG integrations (PDF ingestion, auto-learning) for custom agent behavior.
Oversee autonomous execution tooling and Universal Commerce Protocol (UCP) integration to enable active user journeys (catalog lookups, universal carts, reservations).
Advocate prompt tuning and loss diagnosis while scaling LLM-based AutoRater frameworks to reduce hallucinations and measure conversation factuality.
Partner with Product, Data Science, UX, and core Google platform teams to define Ground Truth datasets, investigative dashboards, and model upgrade standards.
Minimum qualifications
Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
8 years of experience coding in one or more general-purpose programming languages (e.g., Python, Java, C++, or Go) focused on backend system architecture.
Experience building automated LLM evaluation pipelines, automated rating systems (AutoRaters), or defining LLM-specific reliability metrics (e.g., measuring grounding, helpfulness, safety, and hallucination/error reduction).
Experience with backend, distributed systems, or AI/ML systems infrastructure.
Experience managing software engineers, including performance management, mentoring, and team growth.
Preferred qualifications
Master's degree or PhD in Computer Science or related technical field.
3 years of experience working in a complex, matrixed organization.
Experience developing, calibrating, and productionizing LLM-based AutoRater frameworks or automated regression testing.
Experience designing agentic tooling and integrating agents with commerce platforms.
Experience in conversational AI architectures, agent orchestration harnesses, and retrieval-augmented generation (RAG) for knowledge libraries.
Track record of technical leadership partnering with Product Management, Data Science, and UX to build "Ground Truth" datasets and performance-tracking dashboards.
Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
8 years of experience coding in one or more general-purpose programming languages (e.g., Python, Java, C++, or Go) focused on backend system architecture.
Experience building automated LLM evaluation pipelines, automated rating systems (AutoRaters), or defining LLM-specific reliability metrics (e.g., measuring grounding, helpfulness, safety, and hallucination/error reduction).
Experience with backend, distributed systems, or AI/ML systems infrastructure.
Experience managing software engineers, including performance management, mentoring, and team growth.
